# Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

## TL;DR

This paper proposes secure blockchain-based architectures for distributed learning that ensure data privacy, trustworthy training, and fair model sharing among coalition members, demonstrated through medical image analysis.

## Contribution

It introduces novel secure architectures combining encryption and blockchain to enhance privacy, trustworthiness, and fairness in distributed learning systems.

## Key findings

- Effective privacy preservation in distributed learning
- Trustworthy iterative training ensured by blockchain
- Fair model sharing among coalition members

## Abstract

Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equitable sharing of the learned model among each member of the coalition by using adequate encryption and blockchain mechanisms. We exemplify its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.